学习构建和部署机器学习数据科学深度学习项目(Python、Flask、Django、Heruko Cloud)

你会学到什么
学习对数字数据使用数值方法
了解机器学习生命周期的完整产品工作流程。
掌握Python上的机器学习,进行强大的分析
使用Matplotlib用Python创建完全定制的数据可视化。
真实生活案例研究和项目,以了解事情是如何在现实世界中完成的
学习预处理数据、清理数据和分析大数据
构建数据科学和机器学习简历项目的现代组合。
了解为每种类型的问题选择哪种机器学习模型

流派:电子学习| MP4 |视频:h264,1280×720 |音频:AAC,48.0 KHz
语言:英语+中英文字幕(云桥网络 机译)云桥网络|大小:15.3 GB |时长:33h 38m



描述
机器学习不像任何其他技术,但在许多情况下,它是唯一可以解决某些问题的技术。我们需要确保参与项目的所有人对需要什么、过程如何工作有共同的理解,并且我们对手边的工具有一个现实的看法。要将所有这些归结为其核心组成部分,我们可以考虑几个重要的规则:

建立共识,这将确保正确的心态

尽早说明应该如何衡量进展

清楚地交流不同的机器学习概念是如何工作的

承认并考虑继承的不确定性,这是过程的一部分

在本课程中,实际使用数据科学解决业务问题。学习使用Python构建和部署机器学习、数据科学、人工智能、Auto Ml、深度学习、自然语言处理(NLP)网络应用项目(Flask、Django、Heruko、Streamlit Cloud)。


我们将涵盖世界顶级公司所需的完整数据科学和机器学习技术堆栈所需的一切。我们的学生已经在戴尔、谷歌开发者、TCS、Wipro和其他顶级科技公司找到了工作!我们已经利用我们的在线和面对面教学经验构建了课程,提供了一种清晰而结构化的方法,不仅可以指导您理解如何使用数据科学和机器学习库,还可以指导您理解我们为什么使用它们。本课程平衡了实际的真实案例研究和机器学习算法背后的数学理论。

一个数据科学家在美国挣多少钱?

2021年7月15日更新的2.8k薪酬报告显示,美国数据科学家的全国平均薪酬为每年1,20718美元(来源:glassdoor)

按公司、角色、平均基本工资列出的薪资(美元)

脸书数据科学家年收入136,000美元。从1014份薪水分析。

亚马逊数据科学家年收入1,25704美元。从307份薪水分析。

苹果数据科学家的年薪为1,53885美元。从147份薪水分析。

谷歌数据科学家年收入1,48316美元。从252份薪水分析。

IBM数据科学家的年薪为1,32662美元。从388份薪水分析。

微软数据科学家年薪1,338,10美元。从205份薪水分析。

英特尔公司数据科学家的年薪为1,259,30美元。从131份薪水分析。

在本课程中,我们将研究下列45个真实世界的项目:

项目-1: Pan Card回火检测器应用程序-在Heroku上部署

项目-2:犬种预测烧瓶应用

项目-3:图像水印应用程序-部署在Heroku上

项目-4:交通标志分类

项目5:从图像中提取文本应用

项目6:植物病害预测简化应用程序

项目7:车辆检测和计数烧瓶应用

项目-8:创建一个面部交换烧瓶应用程序

项目-9:鸟类物种预测烧瓶应用

项目-10:英特尔图像分类烧瓶应用

项目-11:情感分析Django应用程序-部署在Heroku上

项目-12:损耗率Django应用程序

项目-13:寻找传奇口袋妖怪姜戈应用程序-部署在英雄库

项目-14:人脸检测简化应用

项目-15:猫Vs狗分类烧瓶应用

项目-16:客户收入预测应用程序-在Heroku上部署

项目-17:来自语音预测应用的性别-部署在Heroku上

项目18:餐厅推荐系统

项目-19:幸福排名Django App-部署在Heroku上

项目-20:森林火灾预测Django应用程序-部署在Heroku

项目-21:构建汽车价格预测应用程序-在Heroku上部署

项目-22:构建事务计数姜戈应用程序-部署在赫罗库

项目-23:构建蘑菇预测应用程序-在Heroku上部署

项目-24:在Heroku上部署的谷歌游戏应用评级预测

项目-25:构建银行客户预测Django应用程序-部署在Heroku

项目-26:建造艺术家雕塑成本预测Django应用程序-部署在Heroku

项目-27:构建医疗成本预测Django应用程序-在Heroku上部署

项目-28:网络钓鱼网页分类Django应用程序-在Heroku上部署

项目-29:衣服合身程度预测姜戈应用程序-部署在Heroku上

项目-30:在文本中构建相似性姜戈应用程序-部署在Heroku上

项目-31:使用评估模型(自动模型)预测心脏病发作风险

项目-32:使用Pycaret(自动ML)检测信用卡欺诈

项目-33:使用自动学习进行航班票价预测

项目-34:使用Auto Keras预测汽油价格

项目-35:利用H2O汽车ML进行银行客户流失预测

项目-36:使用端到端部署的TPOT的空气质量指数预测器(自动ML)

项目-37:使用最大似然模型预测降雨&带部署的PyCaret(自动最大似然)

项目-38:使用最大似然法和最大似然法(自动最大似然法)预测比萨饼价格

项目-39:使用TPOT(自动最大似然)预测板球得分

项目-40:使用ML和H2O汽车ML预测自行车租赁数量

项目-41:使用Auto Keras (Auto ML)预测混凝土抗压强度

项目-42:使用自动SK学习(自动ML)预测班加罗尔房价

项目-43:使用PyCaret(自动毫升)预测医院死亡率

项目-44:使用ML和评估自动ML进行员工晋升评估

项目-45:利用最大似然法和H2O自动最大似然法预测饮用水可饮用性

提示(非强制性):制定一个45天的学习计划,每天花1-2小时,在45天内完成45个项目。

成为一名数据科学家、被聘用并开始新职业的唯一途径

Data Science Bootcamp: Build & Deploy 45 Real World Projects

Genre: eLearning | MP4 | Video: h264, 1280×720 | Audio: AAC, 48.0 KHz
Language: English | Size: 15.3 GB | Duration: 33h 38m
Learn To Build & Deploy Machine Learning, Data Science, Deep Learning Projects (Python, Flask, Django, Heruko Cloud)

What you’ll learn
Learn to use NumPy for Numerical Data
Understand the full product workflow for the machine learning lifecycle.
Master Machine Learning on Python, Make powerful analysis
Use Matplotlib to create fully customized data visualizations with Python.
Real life case studies and projects to understand how things are done in the real world
Learn to pre process data, clean data, and analyze large data
Construct a modern portfolio of data science and machine learning resume projects.
Learn which Machine Learning model to choose for each type of problem

Description
Machine learning is not like any other technology, but it is in many cases the only technology that can solve certain problems. We need to ensure that all people involved in the project have a common understanding of what is required, how the process works, and that we have a realistic view of what is possible with the tools at hand. To boil down all this to its core components we could consider a few important rules:

create a common ground of understanding, this will ensure the right mindset

state early how progress should be measured

communicate clearly how different machine learning concepts works

acknowledge and consider the inherited uncertainty, it is part of the process

In This Course, Solve Business Problems Using Data Science Practically. Learn To Build & Deploy Machine Learning, Data Science, Artificial Intelligence, Auto Ml, Deep Learning, Natural Language Processing (NLP) Web Applications Projects With Python (Flask, Django, Heruko, Streamlit Cloud).

We’ll cover everything you need to know for the full data science and machine learning tech stack required at the world’s top companies. Our students have gotten jobs at Dell, Google Developers, TCS, Wipro, and other top tech companies! We’ve structured the course using our experience teaching both online and in-person to deliver a clear and structured approach that will guide you through understanding not just how to use data science and machine learning libraries, but why we use them. This course is balanced between practical real-world case studies and mathematical theory behind the machine learning algorithms.

How much does a Data Scientist make in the United States?

The national average salary for a Data Scientist is US$1,20,718 per year in the United States, 2.8k salaries reported, updated on July 15, 2021 (source: glassdoor)

Salaries by Company, Role, Average Base Salary in (USD)

Facebook Data Scientist makes US$1,36,000/yr. Analyzed from 1,014 salaries.

Amazon Data Scientist makes US$1,25,704/yr. Analyzed from 307 salaries.

Apple Data Scientist makes US$1,53,885/yr. Analyzed from 147 salaries.

Google Data Scientist makes US$1,48,316/yr. Analyzed from 252 salaries.

IBM Data Scientist makes US$1,32,662/yr. Analyzed from 388 salaries.

Microsoft Data Scientist makes US$1,33,810/yr. Analyzed from 205 salaries.

Intel Corporation Data Scientist makes US$1,25,930/yr. Analyzed from 131 salaries.

In This Course, We Are Going To Work On 45 Real World Projects Listed Below:

Project-1: Pan Card Tempering Detector App -Deploy On Heroku

Project-2: Dog breed prediction Flask App

Project-3: Image Watermarking App -Deploy On Heroku

Project-4: Traffic sign classification

Project-5: Text Extraction From Images Application

Project-6: Plant Disease Prediction Streamlit App

Project-7: Vehicle Detection And Counting Flask App

Project-8: Create A Face Swapping Flask App

Project-9: Bird Species Prediction Flask App

Project-10: Intel Image Classification Flask App

Project-11: Sentiment Analysis Django App -Deploy On Heroku

Project-12: Attrition Rate Django Application

Project-13: Find Legendary Pokemon Django App -Deploy On Heroku

Project-14: Face Detection Streamlit App

Project-15: Cats Vs Dogs Classification Flask App

Project-16: Customer Revenue Prediction App -Deploy On Heroku

Project-17: Gender From Voice Prediction App -Deploy On Heroku

Project-18: Restaurant Recommendation System

Project-19: Happiness Ranking Django App -Deploy On Heroku

Project-20: Forest Fire Prediction Django App -Deploy On Heroku

Project-21: Build Car Prices Prediction App -Deploy On Heroku

Project-22: Build Affair Count Django App -Deploy On Heroku

Project-23: Build Shrooming Predictions App -Deploy On Heroku

Project-24: Google Play App Rating prediction With Deployment On Heroku

Project-25: Build Bank Customers Predictions Django App -Deploy On Heroku

Project-26: Build Artist Sculpture Cost Prediction Django App -Deploy On Heroku

Project-27: Build Medical Cost Predictions Django App -Deploy On Heroku

Project-28: Phishing Webpages Classification Django App -Deploy On Heroku

Project-29: Clothing Fit-Size predictions Django App -Deploy On Heroku

Project-30: Build Similarity In-Text Django App -Deploy On Heroku

Project-31: Heart Attack Risk Prediction Using Eval ML (Auto ML)

Project-32: Credit Card Fraud Detection Using Pycaret (Auto ML)

Project-33: Flight Fare Prediction Using Auto SK Learn (Auto ML)

Project-34: Petrol Price Forecasting Using Auto Keras

Project-35: Bank Customer Churn Prediction Using H2O Auto ML

Project-36: Air Quality Index Predictor Using TPOT With End-To-End Deployment (Auto ML)

Project-37: Rain Prediction Using ML models & PyCaret With Deployment (Auto ML)

Project-38: Pizza Price Prediction Using ML And EVALML(Auto ML)

Project-39: IPL Cricket Score Prediction Using TPOT (Auto ML)

Project-40: Predicting Bike Rentals Count Using ML And H2O Auto ML

Project-41: Concrete Compressive Strength Prediction Using Auto Keras (Auto ML)

Project-42: Bangalore House Price Prediction Using Auto SK Learn (Auto ML)

Project-43: Hospital Mortality Prediction Using PyCaret (Auto ML)

Project-44: Employee Evaluation For Promotion Using ML And Eval Auto ML

Project-45: Drinking Water Potability Prediction Using ML And H2O Auto ML

Tip (Not Mandatory): Create A 45 Days Study Plan, Spend 1-2hrs Per Day, Build 45 Projects In 45 Days.

The Only Course You Need To Become A Data Scientist, Get Hired And Start A New Career

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